Furthermore, two layer-specific MLPs are used to convert each hidden state \(h_t\)  into \(\gamma _t \)  and \(\beta _t\)  because each convolution layer has different channel dimensions. Different to conditional instance normalization, conditional batch normalization and conditional deep fusion [1]}, our work no longer takes affine transformation as isolated modules. In contrast, we employ RNN to model the long-term dependency between fusion blocks, which not only forces the fusion blocks to be consistent with one another but also reduces the difficulty of training with skip connections.
